ArticlePloS one2017
Texture analysis of acute myocardial infarction with CT: First experience study.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it, 51 citations in OpenAlex.
- Quality assessment of radiomics research in cardiac CT: a systematic review.European radiology · 2022Pooled it
- Predicting Chronic Myocardial Ischemia Using CCTA-Based Radiomics Machine Learning Nomogram.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2022Trial
- Prediction of acute coronary syndrome within 3 years using radiomics signature of pericoronary adipose tissue based on coronary computed tomography angiography.European radiology · 2022Trial
- Prognostic value of SPECT myocardial perfusion entropy in high-risk type 2 diabetic patients.European journal of nuclear medicine and molecular imaging · 2021Trial
- Radiomics and anaesthetic planning: Quantitative imaging as a new frontier in preoperative risk assessment.World journal of radiology · 2026Review
- Texture analysis on muscle T2 maps can reveal changes in upper trapezius muscles related to primary headache disorders.Scientific reports · 2026Article
- Assessing Postpartum Levator Ani Muscle Recovery: A Feasibility Study on Automated Texture Analysis in Transvaginal Ultrasound.Ultrasound in medicine & biology · 2025Article
- Predicting symptomatic carotid artery plaques with radiomics-based carotid perivascular adipose tissue characteristics: a multicenter, multiclassifier study.BMC medical imaging · 2025Article
- Chest CT-based analysis of radiomic and volumetric differences in epicardial adipose tissue in HFrEF patients with and without AF.BMC cardiovascular disorders · 2025Article
- Radiomics Signature of Aging Myocardium in Cardiac Photon-Counting Computed Tomography.Diagnostics (Basel, Switzerland) · 2025Article
- Development of a clinical prediction model for acute myocarditis using coronary computed tomography angiography-based radiomics.Cardiovascular diagnosis and therapy · 2025Article
- Intraindividual reproducibility of myocardial radiomic features between energy-integrating detector and photon-counting detector CT angiography.European radiology experimental · 2024Article
- Review
- Altered Thyroid Feedback Loop in Klinefelter Syndrome: From Infancy Through the Transition to Adulthood.The Journal of clinical endocrinology and metabolism · 2023Article
- The incremental value of CCTA-derived myocardial radiomics signature for ischemia diagnosis with reference to CT myocardial perfusion imaging.The British journal of radiology · 2023Article
- Radiomics in Cardiac Computed Tomography.Diagnostics (Basel, Switzerland) · 2023Review
- Artificial intelligence in coronary computed tomography angiography: Demands and solutions from a clinical perspective.Frontiers in cardiovascular medicine · 2023Review
- Myocardial Radiomics Texture Features Associated with Increased Coronary Calcium Score-First Results of a Photon-Counting CT.Diagnostics (Basel, Switzerland) · 2022Article
- Cardiac computed tomography radiomics: a narrative review of current status and future directions.Quantitative imaging in medicine and surgery · 2022Review
- Comparison Study of Myocardial Radiomics Feature Properties on Energy-Integrating and Photon-Counting Detector CT.Diagnostics (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo investigate the feasibility and accuracy of texture analysis to distinguish through objective and quantitative image information between healthy and infarcted myocardium with computed tomography (CT). MATERIALS AND
methodsTwenty patients (5 females; mean age 56±10years) with proven acute myocardial infarction (MI) and 20 patients (8 females; mean age 42±15years) with no cardiac abnormalities (hereafter termed controls) underwent contrast-enhanced cardiac CT. Short axis CT images of the left ventricle (LV) were reconstructed at the slice thicknesses 1mm, 2mm, and 5mm. Two independent, blinded readers segmented the LV in controls and patients. Texture analysis was performed yielding first-level features based on the histogram (variance, skewness, kurtosis, entropy), second-level features based on the gray-level co-occurrence matrix (GLCM) (contrast, correlation, energy and homogeneity), and third-level features based on the gray-level run-length matrix (GLRLM).
resultsInter-and intrareader agreement was good to excellent for all histogram (intraclass correlation coefficient (ICC):0.70-0.93) and for all GLCM features (ICC:0.66-0.99), and was variable for the GLRLM features (ICC:-0.12-0.99). Univariate analysis showed significant differences between patients and controls for 2/4 histogram features, 3/4 GLCM and for 6/11 GLRLM features and all assessed slice thicknesses (all,p<0.05). In a multivariate logistic regression model, the single best variable from each level, determined by ROC analysis, was included stepwise. The best model included kurtosis (OR 0.08, 95%CI:0.01-0.65,P = 0.018) and short run high gray-level emphasis (SRHGE, OR 0.97, 95%CI:0.94-0.99,P = 0.007), with an area-under-the-curve (AUC) of 0.90 (95%CI:0.80-0.99). The best results for kurtosis and SRHGE (AUC = 0.78) were obtained at a 5mm slice thickness. A cut-off value of 14.4 for kurtosis+0.013*SRHGE predicted acute MI with a sensitivity of 95% (specificity 55%).
conclusionOur study illustrates the feasibility of texture analysis for distinguishing healthy from acutely infarcted myocardium with cardiac CT using objective, quantitative features, with most reproducible and accurate results at a short axis slice thickness of 5mm.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.